where \(KL\)  denotes the Kullback–Leibler divergence [1]}. Thus, we minimize the sum of loss objectives to train the parameters of the framework:
\(\mathcal {L}_{NFs+CVAEs}(y, \zeta , z, X; \theta _1, \theta _2) = \mathcal {L}_{NFs}(y,(\zeta ,z); \theta _1) + \mathcal {L}_{CVAEs}(X,\zeta ; \theta _2)\) 
